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research-backup
/
roberta-large-semeval2012-mask-prompt-a-nce-classification

Feature Extraction
Transformers
PyTorch
roberta
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use research-backup/roberta-large-semeval2012-mask-prompt-a-nce-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use research-backup/roberta-large-semeval2012-mask-prompt-a-nce-classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="research-backup/roberta-large-semeval2012-mask-prompt-a-nce-classification")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("research-backup/roberta-large-semeval2012-mask-prompt-a-nce-classification")
    model = AutoModel.from_pretrained("research-backup/roberta-large-semeval2012-mask-prompt-a-nce-classification", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
roberta-large-semeval2012-mask-prompt-a-nce-classification
2.85 GB
Ctrl+K
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  • 1 contributor
History: 8 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
da287bc verified over 1 year ago
  • .gitattributes
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  • README.md
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  • analogy.json
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  • classification.json
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  • config.json
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  • merges.txt
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  • model.safetensors
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    Adding `safetensors` variant of this model over 1 year ago
  • pytorch_model.bin
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  • relation_mapping.json
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • trainer_config.json
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  • validation_loss.json
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  • vocab.json
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